Non-invasive risk stratification of immunoglobulin A nephropathy and crescent formation using contrast-enhanced ultrasound combined with serological markers. [PDF]
Quant Imaging Med SurgShen C +8 more
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Integrated Assessment of Sarcopenia in Patients with Gastric Cancer Using Deep Learning and Radiomics. [PDF]
J Cachexia Sarcopenia MuscleZhi H +12 more
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Diagnostic Utility of Platelet Indices in Differentiating Hypoproductive and Hyperdestructive Thrombocytopenia: A Cross-Sectional Study From North India. [PDF]
CureusChoudhary R +4 more
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MRI-based extensor carpi ulnaris tendon cross-sectional area as a quantitative biomarker for extensor carpi ulnaris tenosynovitis. [PDF]
Front Digit HealthLee JM +7 more
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Adipokine patterns in type 1 and type 2 diabetes mellitus: comparative analysis of adiponectin-leptin ratio, visfatin, and TNF-α. [PDF]
Front Endocrinol (Lausanne)Wu B, He H, Zeng J, Lai X, Bai Y.
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Features of the Area under the Receiver Operating Characteristic (ROC) Curve. A Good Practice [PDF]
The Stata Journal, 2016 The area under the receiver operating characteristic (ROC) curve is a measure of discrimination ability used in diagnostic and prognostic research. The ROC plot is usually represented without additional information about decision thresholds used to generate the graph. In our article, we show that adding at least one or more informative cutoff points on
David Lora, Contador I
exaly +3 more sources
Receiver operating characteristic (ROC) curve for medical researchers
Indian Pediatrics, 2011 Sensitivity and specificity are two components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard. Receiver operating characteristic (ROC) curve is the plot that depicts the trade-off between the sensitivity and (1-specificity) across a series of cut-off points when the diagnostic test is continuous
Rajeev Kumar, Abhaya Indrayan
exaly +3 more sources
What's under the ROC? An Introduction to Receiver Operating Characteristics Curves [PDF]
Canadian Journal of Psychiatry, 2007 It is often necessary to dichotomize a continuous scale to separate respondents into normal and abnormal groups. However, because the distributions of the scores in these 2 groups most often overlap, any cut point that is chosen will result in 2 types of errors: false negatives (that is, abnormal cases judged to be normal) and false positives (that is,
David Streiner, John Cairney
exaly +6 more sources